First, let's make sure we have the correct libraries loaded. The project file of Leiningen (https://github.com/technomancy/leiningen), the project.clj file, should contain these dependencies (although you may be able to use more up-to-date versions):

:dependencies [[org.clojure/clojure "1.4.0"]
               [incanter/incanter-core "1.4.1"]
               [incanter/incanter-io "1.4.1"]]

Also, in your REPL or in your file, include these lines:

(use 'incanter.core
     'incanter.io)

Finally, I have a file named data/small-sample.csv that contains the following data:

Gomez,Addams,father
Morticia,Addams,mother
Pugsley,Addams,brother
Wednesday,Addams,sister
…

You can download this file from http://www.ericrochester.com/clj-data-analysis/data/small-sample.csv. There's a version with a header row at http://www.ericrochester.com/clj-data-analysis/data/small-sample-header.csv.

Using Clojure and Incanter makes a lot of common tasks easy. This is a good example of that.

We've taken some external data, in this case from a CSV file, and loaded it into an Incanter dataset. In Incanter, a dataset is a table, similar to a sheet in a spreadsheet or a database table. Each column has one field of data, and each row has an observation of data. Some columns will contain string data (all of the columns in this example did), some will contain dates, some numeric data. Incanter tries to detect automatically when a column contains numeric data and coverts it to a Java int or double. Incanter takes away a lot of the pain of importing data.

If we don't want to involve Incanter—when you don't want the added dependency, for instance—data.csv is also simple (https://github.com/clojure/data.csv). We'll use this library in later chapters, for example, in the recipe Lazily processing very large datasets of Chapter 2, Cleaning and Validating Data.